Interactive segmentation in MRI for orthopedic surgery planning: bone tissue.

Purpose: Planning orthopedic surgeries is commonly performed in computed tomography (CT) images due to the higher contrast of bony structure. However, soft tissues such as muscles and ligaments that may determine the functional outcome of a procedure are not easy to identify in CT, for which fast and accurate segmentation in MRI would be desirable. To be usable in daily practice, such method should provide convenient means of interaction for modifications and corrections, e.g., during perusal by the surgeon or the planning physician for quality control.

Methods: We propose an interactive segmentation framework for MR images and evaluate the outcome for segmentation of bones. We use a random forest classification and a random walker-based spatial regularization. The latter enables the incorporation of user input as well as enforcing a single connected anatomical structures, thanks to which a selective sampling strategy is proposed to substantially improve the supervised learning performance.

Results: We evaluated our segmentation framework on 10 patient humerus MRI as well as 4 high-resolution MRI from volunteers. Interactive humerus segmentations for patients took on average 150 s with over 3.5 times time-gain compared to manual segmentations, with accuracies comparable (converging) to that of much longer interactions. For high-resolution data, a novel multi-resolution random walker strategy further reduced the run time over 20 times of the manual segmentation, allowing for a feasible interactive segmentation framework.

Conclusions: We present a segmentation framework that allows iterative corrections leading to substantial speed gains in bone annotation in MRI. This will allow us to pursue semi-automatic segmentations of other musculoskeletal anatomy first in a user-in-the-loop manner, where later less user interactions or perhaps only few for quality control will be necessary as our annotation suggestions improve.